Systems and methods for planning high altitude platform-based communication networks

ABSTRACT

A method for planning a high altitude platform-based communication network includes aggregating data from at least one data source, wherein the data includes environmental data. Based on the aggregated data, a plurality of network expansion potential scores are computed according to geographic location. A visual output is generated based on the computed plurality of network expansion potential scores.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.15/915,049, filed Mar. 7, 2018, the entire disclosure of which isincorporated herein by reference.

BACKGROUND

Automated network planning for terrestrial wireless communicationnetworks typically involves the use of algorithms that simulate radiofrequency (RF) signal propagation and rely upon models of terrestrialcommunication towers, population data, and/or existing connectivity datato determine where to place new terrestrial communication towers.However, communication networks projected from high altitude platforms(HAPs), such as networks in which communication nodes are embodied asaerial vehicles floating in the atmosphere, are subject to technicalchallenges not faced by terrestrial networks and unaddressed bytraditional network planning approaches. For instance, in HAP-basednetworks the communication nodes are subject to environmental influence,such as stratospheric winds, move vertically and laterally relative tothe earth, and thus have a highly dynamic state. Additionally, the costof providing service via an HAP-based communication network in a regiondepends on the navigability of nodes through the atmosphere in andaround that region, and the navigability varies over time based uponseason, weather, and/or other factors. Such a cost is also unaddressedby traditional network planning approaches. In view of the foregoing, itwould be beneficial to have improved systems and methods for planningcommunication networks projected from HAPs.

SUMMARY

In one aspect, this disclosure describes a method for planning anHAP-based communication network. In embodiments, the method includesaggregating data from at least one data source, wherein the dataincludes environmental data. Based on the aggregated data, a pluralityof network expansion potential scores are computed according togeographic location. A visual output is generated based on the computedplurality of network expansion potential scores.

In embodiments, the at least one data source includes population dataaccording to at least one of geographic location or time.

In embodiments, the at least one data source includes network presenceestimates according to at least one of geographic location or time.

In embodiments, the environmental data includes wind pattern dataaccording to at least one of geographic location, altitude, or time.

In embodiments, the at least one data source includes a source of dataregarding a navigation efficiency of a stratospheric platform accordingto at least one of geographic location, altitude, or time.

In embodiments, the method further comprises computing an ability of anaerial vehicle to remain over a service region based on the dataregarding the navigation efficiency of the stratospheric platform.

In embodiments, the at least one data source includes mobile networkstatistical intelligence data.

In embodiments, the method further comprises retrieving, from the atleast one data source, average revenue per user according to at leastone of geographic location or time.

In embodiments, the method further comprises computing a plurality ofrevenue scores according to geographic location, with the computing ofthe network expansion potential scores being based on the computedplurality of revenue scores.

In embodiments, the method further comprises computing a plurality ofcost-of-service scores according to geographic location, with thecomputing of the network expansion potential scores being based on thecomputed plurality of cost-of-service scores.

In embodiments, the method further comprises computing a plurality ofrevenue scores according to geographic location; and computing aplurality of cost-of-service scores according to geographic location,wherein the computing of the network expansion potential scores is basedon the plurality of revenue scores and the plurality of cost-of-servicescores.

In embodiments, the visual output includes a graphical representation ofthe computed plurality of revenue scores.

In embodiments, the visual output includes a graphical representation ofthe plurality of cost-of-service scores.

In embodiments, the visual output includes a graphical representation ofthe computed plurality of network expansion potential scores.

In embodiments, at least one of the aggregating, the computing, or thegenerating, is periodically repeated based on updated data.

In another aspect, the present disclosure describes a system forplanning an HAP-based communication network. The system comprises: atleast one data source, a user interface, and at least one computingdevice communicatively coupled to the at least one data source. The atleast one data source includes a data source storing environmental data.The at least one computing device is configured to: (1) aggregate datafrom the at least one data source, wherein the data includes theenvironmental data; (2) compute, based on the aggregated data, aplurality of network expansion potential scores according to geographiclocation; (3) generate a visual output generated based on the computedplurality of network expansion potential scores; and (4) cause the userinterface to display the generated visual output.

In embodiments, the environmental data includes wind pattern dataaccording to at least one of geographic location, altitude, or time.

In embodiments, the at least one data source includes a source of dataregarding a navigation efficiency of a stratospheric platform accordingto at least one of geographic location, altitude, or time.

In embodiments, the at least one computing device is further configuredto compute an ability of an aerial vehicle to remain over a serviceregion based on the data regarding the navigation efficiency of astratospheric platform.

In another aspect, the present disclosure describes a non-transitorycomputer-readable storage medium storing a program for planning anHAP-based communication network. In particular, the program includesinstructions which, when executed by a processor, causes a computingdevice to implement a method for planning an HAP-based communicationnetwork planning. The method comprises: (1) aggregating data from atleast one data source, wherein the data includes environmental data; (2)computing, based on the aggregated data, a plurality of cost-of-servicescores according to geographic location; and (3) generating a visualoutput based on the computed plurality of cost-of-service scores.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and features of the present systems and methods forplanning HAP-based communication networks are described herein belowwith references to the drawings, wherein:

FIG. 1 is a schematic block diagram showing an illustrative system forplanning an HAP-based communication network, in accordance with anembodiment of the present disclosure;

FIG. 2 is a flowchart showing an illustrative method for planning anHAP-based communication network, in accordance with a first embodimentof the present disclosure;

FIG. 3 is a flowchart showing an illustrative method for planning anHAP-based communication network, in accordance with a second embodimentof the present disclosure; and

FIG. 4 is a schematic block diagram of an illustrative embodiment of acomputing device that may be employed in various embodiments of thepresent system, for instance, as part of the system or components ofFIG. 1, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

The present disclosure is directed to systems and methods for planningcommunication networks generated from base station some of which areaboard aerial vehicles, such as high altitude platforms (HAPs) likestratospheric balloons. More specifically, the systems and methods ofthe present disclosure enable worldwide market opportunities forcommunication networks projected from HAPs to be identified based ontraditional parameters as well as atmospheric impacts and other factors,thereby facilitating improvements to system efficiency and networkutility. In one aspect, the present disclosure describes a collection ofmachine-aided optimization systems that do planet-scale operationsresearch to guide deployments of HAP-based communication networks,taking into account environment influence and the highly dynamic stateof network nodes. One example of such an HAP-based communication networkis one that is projected from stratospheric platforms, e.g., aerialvehicles, airships, or other unmanned aerial vehicles, capable ofrelaying connectivity and/or internet access between ground stations andremote users with LTE handsets. The systems and methods of the presentdisclosure, among other things, tackle the problems of estimatingopportunity to capture market volume and estimating economies ofagglomeration.

The systems and methods described herein are highly scalable and, insome aspects, are automated and do not require analyst knowledge in theloop. For example, in one example embodiment herein, a system and methodare provided that, by push of one or more buttons or a regular periodicautomated run, generate a ranking of business opportunities worldwide.In this manner, ranking conclusions may be efficiently reexamined basedon new market data, as market assumptions change, and as flight vehiclesevolve. In a further aspect, a system and method of the presentdisclosure involves a pipeline that performs an amount of rote workimpractical for an analyst and generates information that can be used toexploit the economies of agglomeration of network deployment in multipleregions.

FIG. 1 shows an example system 100 for planning an HAP-basedcommunication network, in accordance with various embodiments herein.The system 100 includes one or more computing devices 112, one or moreuser interfaces 114, and a variety of data sources 102, 104, 106, 108,110. The data sources 102, 104, 106, 108, 110 are communicativelycoupled to the computing device(s) 112 by way of respectivecommunication paths, and the computing device(s) 112 are communicativelycoupled to the user interface(s) 114 by way of one or more additionalcommunication paths. The computing device(s) 112 may generally be anytype of computing device, such as a personal computer, server, and/orthe like. Details regarding one example embodiment of the computingdevice(s) 112 are shown in FIG. 4, which is described below. The datasources include a population database 102 (storing, for example,population density estimates according to geographic location and/orother types of population-related data); a network presence estimatesdatabase 104 (storing, for example, estimates of network presenceaccording to geographic location); a database 106 storing, for example,atmospheric reanalysis models, such as from the European Centre forMedium-Range Weather Forecasts (ECMWF) or the National Oceanic andAtmospheric Administration (NOAA), that can be post-processed toindicate efficiency of stratospheric navigation according to geographiclocation and/or altitude; a GSMA (or LTE or other network) intelligencedatabase 108; and/or other types of databases 110. In general, and asdescribed further below, the computing device(s) 112 execute one or morealgorithms for planning an HAP-based network, utilizing data from one ormore of the data sources 102, 104, 106, 108, 110, and generate one ormore items of visual output, such as market opportunity rankings bygeographic region, rankings of agglomeration of two or more geographicregions and/or the like, to be provided via the user interface(s) 114.

Having provided an overview of the system 100 in the context of FIG. 1,reference is now made to FIG. 2, which is a flowchart illustrating anexample method 200 for planning an HAP-based network, in accordance withan embodiment of the present disclosure. In particular, FIG. 2illustrates an example method 200 for planning an HAP-basedcommunication network by utilizing the computing device(s) 112 and/ordata sources 102, 104, 106, 108, 110 to identify mobile networkexpansion potential by geographic region, and generate a correspondingoutput via the user interface(s) 114. Mobile network expansion (MNE), inthis context, involves an HAP-based communication network providerpartnering with a carrier to deliver a network as a service thatfunctionally replaces an expansion of the partner's radio accessnetwork. This effectively extends the reach of their network to a largergeographic footprint.

The MNE potential estimation is implemented as a data-processingpipeline run by a system (e.g., including one or more of the computingdevice(s) 112) that launches processing pipelines on multiple machinesto do parallel computation. A detailed flow chart of the pipeline isillustrated in FIG. 2. At blocks 202, 204, and 206, the pipeline startswith parallel data fetch from various sources. More particularly, atblock 202, population density data is read from the population database102. At block 204, estimates of existing terrestrial networks and/orprojected expansion of terrestrial networks, obtained for instance fromthe network presence estimates database 104, are joined to assess wherethere are unconnected or under-connected people.

In one example, unserved and underserved users are grouped in a singleblock rather than segmenting the data by carrier, but the system 100 canbe used to ask questions about new market entrants and assumptions onadoption rates by segmenting data by carrier. The system 100 rankspotential by geographic region, such as country, and also provides anestimate of where in each region/country the largest impact can be made.Potential market is approximated as a function of revenue and cost ofrevenue. Due to uncertainty in accurately predicting both of thoseterms, in some examples, a ranking of opportunity around the globe isgenerated rather than a forecast of profits over different geographies.

At block 206, reanalysis models of stratospheric data, such as from NOAAor the ECMWF, are read from the database 106 and processed to estimatethe navigation efficiency of a stratospheric platform according togeographic location.

At block 208, a number of data sources, including: (1) raw populationestimates, such as from the population database 102 (block 202); (2)existing or estimated future network presence (block 204); (3) dataregarding the estimated navigation efficiency of a stratosphericplatform (e.g., from post-processed ECMWF) weather data in the database106), which is an estimate of ease of a stratospheric platform beingkept in place, and corresponds to diversity of winds aloft (block 206);(4) estimates of amounts of time required to return to locations usingatmospheric wind data (e.g., estimated based on extremely post-processedECMWF weather data in the database 106), which estimate transit andreturn times of vehicles over long (multi-day) duration flights; (5)long-term evolution (LTE) network simulation data; and/or (6) worldwideaverage revenue per user (ARPU) data, over a worldwide lattice ofgeographic cells, such as S2 cells (which, as one of skill in the artwould appreciate, are spatially indexed cells used to uniquely representgeographic locations across the Earth using spherical geometry), over arelatively fine granularity (block 212), are joined by cell area.

At block 210, the MNE potential scores for each S2 cell area arecomputed according to any suitable algorithm, for example, based onequation (4) shown below, the ARPU data from block 212 (described below)and/or other information. In order to provide continuous service in aregion, when an aerial vehicle drifts away, another aerial vehicle willarrive to provide service. Overprovisioning is a strategy used in fleetplanning to utilize additional flight systems to cover the gap inservice when aerial vehicles drift away. This means that the cost ofservice is not only dependent on the number of people to serve in theregion, but also vary depending on the stratospheric wind at differenttimes. One aerial vehicle may be needed to serve an area when the windis calm, but on “bad” days when it is volatile in the stratosphere, manymore aerial vehicles are needed.

Although other suitable computational approaches are also contemplated,the following description and equations are provided to illustrate onecomputational approach, by way of example and not limitation. For eachS2 cell, at block 210, a function proportional to revenue and cost ofservice may be generated. The cost of service for an area is the totalnumber of aerial vehicles required to serve the people on the groundover a period of time. Cost of service for an S2 cell, in some examples,may be estimated to be proportional to:C=o ₁ g+o ₂(1−g)  (1)where g is the percentage of days the vehicle can be kept on stationover the service region, o₁ is the number of aerial vehicles required ata particular time during those periods, and o₂ is the number of aerialvehicles required to cover the cell plus the number of aerial vehiclesthat need to be added to continue to cover the cell through the “bad”time. Pure cost per day can be recovered by multiplying the cost ofservice C by the total cost of a flight system per day.

There are different ways to estimate this, but the most basic packageinvolves using the estimated navigation efficiency of a stratosphericplatform (block 206) to estimate g and using a constant andrepresentative o₁ and o₂ to create a reasonable ratio. o₁ can beimproved via using access layer (e.g., LTE) network simulation data toget the appropriate number of aerial vehicles for a particulargeography. In reality, the ratio between o₁ and o₂ varies over time. o₂can be improved by multiplying o₁ by the overprovisioning numberestimated by the amount of time required to return to the currentlocation using atmospheric wind data. In practice the general trend canbe assessed via the most basic estimate (estimated navigation efficiencyof a stratospheric platform being the dominating factor) and a quitegood estimate can be had by varying o₂ regionally.

Although other suitable estimate approaches are possible, in oneexample, revenue for an S2 cell may be estimated to be proportional to afixed revenue (which can be zero) plus the region/country's ARPU amultiplied by the average revenue generated at this location r_(l). Thiscan be broken into two terms to represent full revenue collection duringgood steering times and a fraction of revenue based on theoverprovisioning plan to be deployed

as compared to the overprovisioning required for 100% availability.R=r+a[gr _(l)+(1−g)r _(l) min(1,δ₂ /o ₂)]  (2)

If, for example, limited fleet size constraints were not modeled, thiscan be reduced to the much simpler form:R=r+ar _(l)  (3)

ARPUs read at block 212 may, in this example, be input data to the scorecomputation at block 210. Estimates of average revenues (variedworldwide) are based on any model of the revenue generated per market,such as a revenue sharing model. For example, the revenue to be sharedmay be estimated based on uncovered (underserved and unserved)population density. One can also consider market adoption rates andusers available per potential telecom partner in this calculation.

If a profit forecast were to be attempted, costs would be subtractedfrom revenue. On the other hand, a ranking of market potential may becomputed in any of a variety of ways. For example, potential may beconsidered to be the quotient of the two estimates, as shown by equation(4) below.P=R/C  (4)

This creates a dimension-free quantity that is used for a few purposes:First potential can be plotted as a visual inspection. Second, thequantity can be used to generate service regions for various potentialpartners using computer vision-style connected component techniques,e.g., flood fill. Third, the quantity can be used at block 214 to rankcountries (and partners) throughout the world in order of potential. Forinstance, based on the S2 cell-level potentials computed at block 210, aflood fill algorithm may be utilized at block 214 to identify a numberof high-potential, connected regions in a region/country and mark themas good potential service regions. Owing to the scalability of themethod 200, estimates can be easily adjusted as market assumptionschange, terrestrial network or population data is updated, or the flightsystem is modified.

At block 216, a coarse, worldwide gridded estimated projection ofpotential is generated as a unitless score that grows higher with moreavailable users (scaled by regional ARPU) and lowers as it becomesincreasingly expensive for an HAP-based communication network providerto provide service in that area. Region/country potential score isaggregated based on the estimates over all the S2 cells in the area.This dimension-free quantity of scoring is easy to be plotted as aworldwide heat map for visual inspection or apply computer vision-styleconnected component techniques, such as flood fill, to generate higherpotential service regions within the countries. It also helps thepartnerships team set priorities with the ability to rank countries andtelco partners throughout the world.

Reference is now made to FIG. 3, which is a flowchart illustrating anexample method 300 for planning an HAP-based network, in accordance withanother embodiment of the present disclosure. In particular, FIG. 3illustrates an example method 300 of utilizing regional clustering forHAP-based communication network planning. Serving data via high altitudeaerial vehicles to some specific groups of countries can be far moreefficient than other groups of countries or a single country. Aerialvehicles in service will leave the targeted region due to winds fromtime to time. Additional flight systems are used to cover that gap inservice, which is referred to as overprovisioning. For theoverprovisioned flights, the network service provider bears the costsfor that flight system without it generating revenue. However, if thataerial vehicle's transit back to the original service region was overanother service region the utilization of all the flight systems wouldbe higher. Thus, not every group of service areas is equally efficient.

Patterns describing the economies of agglomeration of specific groups ofcountries or regions may be mined and used for business deploymentplanning. This clustering process generally involves mining historicalwind data, predicting transit and return times of aerial vehicles at allparts of the world, and looking for the economies of agglomeration ofspecific groups of countries or regions where aerial vehicles tend totravel in loops across various times of the year. Using the knowledge ofwind patterns to predict these effects enables a network provider tomake better business planning decisions, improve efficiency of thefleet, and have a significant impact on the overall business over longerhorizons. The amount of time-series weather data, simulation of flighttrajectories, and exploration space make this problem non-trivial.Service regions within each geography, e.g., country, are selected asgraph nodes for exploration. A distributed system is developed toefficiently reflow simulated trajectories of long-duration flights overa cluster of distributed machines to estimate transit times betweenthese different regions. The pipeline has three main phases: (1) graphextraction, (2) affinity estimation, and (3) cluster discovery.

A graph is incrementally generated at block 302 by asking thedistributed system to build and retrieve connections (edge) and transittimes (weight) via a Monte Carlo sampling-based procedure. Graphextraction is the process of choosing a set of nodes corresponding tocountries or service regions. The naive algorithm involves pickingarbitrary points within each region/country, e.g., the center point orthe S2 cell with the highest MNE potential as graph nodes. In otherembodiments, the algorithm may rely more heavily on clusters of MNEpotential to extract multiple regions within single countries or droppolitical boundaries altogether.

The affinity graph is thus learned, stored, and improved over time atblock 304. Monte Carlo sampling of transit times between regions atdifferent historical periods of time is used to incrementally refineestimates of affinities on each graph edge. Affinity estimation usesMonte Carlo sampling to avoid requiring an exhaustive and intractableamount of simulation, and to enable clusters to emerge in the discoveryprocess with a relatively small amount of samples. Estimates of theamounts of time required to return from one location to another,generated using atmospheric wind data, are used as estimates of affinitybetween regions.

A controller job is used to run the Monte Carlo portion of the code andperiodically serialize the graph. This ability to create estimates oftransit times between regions on the fly avoids the need to materializeall transit time estimates for every region in the world.

According to one non-limiting example, a core data structure is used inthe form of a directed graph with nodes representing regions and edgesstoring affinity from the source region to destination region. One couldimagine a wide range of summary statistics being useful in the discoveryphase. The simplest is perhaps the mean transit time. To preserveflexibility a coarse histogram of transit times on each edge may bekept. Although other suitable algorithms are also contemplated, in someexamples, an algorithm such as the following algorithm may be used togenerate affinity estimates.

G ← load graph from disk (potentially with empty edges from graphextraction) for i = 1 to anytime:  t0 ← sample time in our corpus ofwinds  prompt reflow cluster to load wind estimates from [t0, t0 + 30days]  for j = 1 to K:  dest_node ← sample country from graph  promptreflow cluster to estimate transit times with target at the S2 cells for dest_node (denote estimates as V)  for source_node in graph:   addV(S2 on source_node) to affinity(source_node, dest_node)   insertaffinity(source_node, dest_node) back into G  if i % C == 0: checkpointserialized graphThe above pseudocode, which is provided by way of example and notlimitation, can be structured as a service that runs indefinitely andprovides incrementally improving affinity estimates by snapshotting thegraph periodically.

At block 306, the region clustering phase takes the graph from block 304as input and runs standard graph clustering techniques. For instance,with the affinity graph generated at block 304, graph clusteringalgorithms are used to find pseudo-cliques to identify the economies ofagglomeration of countries that are best suited to deploy HAP-basedcommunication network service as a group. In some embodiments, the sametype of clustering used to find pseudo-cliques on social graphs is usedat block 306 to compute clusters of interest. Since this graph isrelatively small relative to most social graphs, exact methods, whichmay be inefficient methods, and in-memory computation may be used. Asummary function may be used to map the histogram on each edge to ascalar affinity. In some embodiments, this graph (and the histogramsummaries) may be viewed as time-varying. This is particularly usefulfor detecting that pseudo-periodic disturbances, such as seasonalpatterns or trends spanning multiple years, e.g., the quasi-biennialoscillation, destroy the affinities within a cluster.

The clusters learned at block 306 can be tested via forward simulationof the network system and analyzed as desired. Using the knowledge ofthe wind patterns to predict these effects enables an HAP-basedcommunication network provider to make better business planningdecisions, improve efficiency of the fleet, and have a significantimpact in the overall business over longer horizons.

FIG. 4 is a schematic block diagram of a computing device 400 that maybe employed in accordance with various embodiments described herein.Although not explicitly shown in FIG. 1, in some embodiments, thecomputing device 400, or one or more of the components thereof, mayfurther represent one or more components (e.g., the computing device(s)112, the user interface(s) 114, and/or the like) of the system 100. Thecomputing device 400 may, in various embodiments, include one or morememories 402, processors 404, display devices 406, network interfaces408, input devices 410, and/or output modules 412. The memory 402includes non-transitory computer-readable storage media for storing dataand/or software that is executable by the processor 404 and whichcontrols the operation of the computing device 400. In embodiments, thememory 402 may include one or more solid-state storage devices such asflash memory chips. Alternatively, or in addition to the one or moresolid-state storage devices, the memory 402 may include one or more massstorage devices connected to the processor 404 through a mass storagecontroller (not shown in FIG. 4) and a communications bus (not shown inFIG. 4). Although the description of computer readable media includedherein refers to a solid-state storage, it should be appreciated bythose skilled in the art that computer-readable storage media may be anyavailable media that can be accessed by the processor 404. That is,computer readable storage media includes non-transitory, volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Examples of computer-readable storage media include RAM,ROM, EPROM, EEPROM, flash memory or other solid state memory technology,CD-ROM, DVD, Blu-Ray or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which may be used to store the desired informationand which can be accessed by computing device 400.

In some embodiments, the memory 402 stores data 414 and/or anapplication 416. In some aspects the application 416 includes a userinterface component 418 that, when executed by the processor 404, causesthe display device 406 to present a user interface, for example agraphical user interface (GUI) (not shown in FIG. 4). The networkinterface 408, in some embodiments, is configured to couple thecomputing device 400 and/or individual components thereof to a network,such as a wired network, a wireless network, a local area network (LAN),a wide area network (WAN), a cellular network, a Bluetooth network, theInternet, and/or another type of network. The input device 410 may beany device by means of which a user may interact with the computingdevice 400. Examples of the input device 410 include without limitationa mouse, a keyboard, a touch screen, a voice interface, a computervision interface, and/or the like. The output module 412 may, in variousembodiments, include any connectivity port or bus, such as, for example,a parallel port, a serial port, a universal serial bus (USB), or anyother similar connectivity port known to those skilled in the art.

The embodiments disclosed herein are examples of the present systems andmethods and may be embodied in various forms. For instance, althoughcertain embodiments herein are described as separate embodiments, eachof the embodiments herein may be combined with one or more of the otherembodiments herein. Specific structural and functional details disclosedherein are not to be interpreted as limiting, but as a basis for theclaims and as a representative basis for teaching one skilled in the artto variously employ the present information systems in virtually anyappropriately detailed structure. Like reference numerals may refer tosimilar or identical elements throughout the description of the figures.

The phrases “in an embodiment,” “in embodiments,” “in some embodiments,”or “in other embodiments” may each refer to one or more of the same ordifferent embodiments in accordance with the present disclosure. Aphrase in the form “A or B” means “(A), (B), or (A and B).” A phrase inthe form “at least one of A, B, or C” means “(A); (B); (C); (A and B);(A and C); (B and C); or (A, B, and C).”

The systems and/or methods described herein may utilize one or morecontrollers to receive various information and transform the receivedinformation to generate an output. The controller may include any typeof computing device, computational circuit, or any type of processor orprocessing circuit capable of executing a series of instructions thatare stored in a memory. The controller may include multiple processorsand/or multicore central processing units (CPUs) and may include anytype of processor, such as a microprocessor, digital signal processor,microcontroller, programmable logic device (PLD), field programmablegate array (FPGA), or the like. The controller may also include a memoryto store data and/or instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform one or moremethods and/or algorithms. In example embodiments that employ acombination of multiple controllers and/or multiple memories, eachfunction of the systems and/or methods described herein can be allocatedto and executed by any combination of the controllers and memories.

Any of the herein described methods, programs, algorithms or codes maybe converted to, or expressed in, a programming language or computerprogram. The terms “programming language” and “computer program,” asused herein, each include any language used to specify instructions to acomputer, and include (but is not limited to) the following languagesand their derivatives: Assembler, Basic, Batch files, BCPL, C, C+, C++,Delphi, Fortran, Java, JavaScript, machine code, operating systemcommand languages, Pascal, Perl, PL1, scripting languages, Visual Basic,metalanguages which themselves specify programs, and all first, second,third, fourth, fifth, or further generation computer languages. Alsoincluded are database and other data schemas, and any othermeta-languages. No distinction is made between languages which areinterpreted, compiled, or use both compiled and interpreted approaches.No distinction is made between compiled and source versions of aprogram. Thus, reference to a program, where the programming languagecould exist in more than one state (such as source, compiled, object, orlinked) is a reference to any and all such states. Reference to aprogram may encompass the actual instructions and/or the intent of thoseinstructions.

Any of the herein described methods, programs, algorithms or codes maybe contained on one or more non-transitory computer-readable ormachine-readable media or memory. The term “memory” may include amechanism that provides (in an example, stores and/or transmits)information in a form readable by a machine such a processor, computer,or a digital processing device. For example, a memory may include a readonly memory (ROM), random access memory (RAM), magnetic disk storagemedia, optical storage media, flash memory devices, or any othervolatile or non-volatile memory storage device. Code or instructionscontained thereon can be represented by carrier wave signals, infraredsignals, digital signals, and by other like signals.

The foregoing description is only illustrative of the present systemsand methods. Various alternatives and modifications can be devised bythose skilled in the art without departing from the disclosure.Accordingly, the present disclosure is intended to embrace all suchalternatives, modifications and variances. The embodiments describedwith reference to the attached drawing figures are presented only todemonstrate certain examples of the disclosure. Other elements, steps,methods, and techniques that are insubstantially different from thosedescribed above and/or in the appended claims are also intended to bewithin the scope of the disclosure.

What is claimed is:
 1. A method for determining mobile network expansionusing a high altitude platform-based communication system, the methodcomprising: identifying, by one or more processors, an uncoveredpopulation density in one or more geographic regions; evaluating, by theone or more processors, one or more atmospheric models for operation ofa set of high altitude platforms in the stratosphere; determining, bythe one or more processors based on the identified uncovered populationdensity in the one or more geographic regions and the evaluated one ormore atmospheric models for the set of high altitude platforms, mobilenetwork expansion potential scores for each of the one or moregeographic regions; generating, by the one or more processors forimplementation of a mobile network expansion, information representingthe determined mobile network expansion potential scores for each of theone or more geographic regions; and outputting the generated informationrepresenting the determined mobile network expansion potential scoresfor presentation to one or more users.
 2. The method of claim 1, whereindetermining the mobile network expansion potential scores for each ofthe one or more geographic regions includes calculating a cost ofservice for subregions in the one or more geographic regions.
 3. Themethod of claim 2, wherein calculating the cost of service for eachsubregion includes analyzing a total number of high altitude platformsrequired to serve potential customers of the uncovered populationdensity over a selected period of time.
 4. The method of claim 1,wherein determining the mobile network expansion potential scores foreach of the one or more geographic regions includes comparing a cost ofproviding the set of high altitude platforms against an expansion of aterrestrial radio access network.
 5. The method of claim 1, whereindetermining the mobile network expansion potential scores for each ofthe one or more geographic regions includes evaluating whether the setof high altitude platforms will be able to provide continuous service inthe one or more geographic regions.
 6. The method of claim 5, whereinevaluating whether the set of high altitude platforms will be able toprovide continuous service includes determining an overprovisioningstrategy for the set of high altitude platforms.
 7. The method of claim1, wherein determining the mobile network expansion potential scores foreach of the one or more geographic regions includes evaluating a stagingcriteria associated with the set of high altitude platforms.
 8. Themethod of claim 1, wherein determining the mobile network expansionpotential scores for each of the one or more geographic regions includesestimating navigation efficiency for each platform of the set of highaltitude platforms.
 9. The method of claim 1, wherein the uncoveredpopulation density includes one or both of underserved and unservedpopulations within the one or more geographic locations.
 10. The methodof claim 1, wherein evaluating the one or more atmospheric models foroperation of the set of high altitude platforms in the stratosphereincludes evaluating atmospheric wind data for the stratosphere.
 11. Themethod of claim 1, wherein determining the mobile network expansionpotential scores for each of the one or more geographic regions furtherincludes evaluating access network information.
 12. The method of claim11, wherein the access network information is associated with a LongTerm Evolution (LTE) network.
 13. The method of claim 1, wherein the setof high altitude platforms includes one or more balloons configured tooperate in the stratosphere.
 14. The method of claim 1, whereinoutputting the generated information representing the determined mobilenetwork expansion potential scores includes presenting the generatedinformation on a display device.
 15. A system for determining mobilenetwork expansion using a high altitude platform-based communicationsystem, the system comprising: memory storing one or more atmosphericmodels for operation of a set high altitude platforms in thestratosphere; and a computing system having one or more processorsoperatively coupled to the memory, the one or more processors beingconfigured to: identify an uncovered population density in one or moregeographic regions; evaluate the one or more atmospheric models foroperation of the set of high altitude platforms; determine, based on theidentified uncovered population density in the one or more geographicregions and the evaluation of the one or more atmospheric models for theset of high altitude platforms, mobile network expansion potentialscores for each of the one or more geographic regions; generate, forimplementation of a mobile network expansion, information representingthe determined mobile network expansion potential scores for each of theone or more geographic regions; and output the generated informationrepresenting the determined mobile network expansion potential scoresfor presentation to one or more users.
 16. The system of claim 15,wherein a determination of the mobile network expansion potential scoresfor each of the one or more geographic regions includes one or more of:calculation of a cost of service for subregions in the one or moregeographic regions; comparison of a cost of providing the set of highaltitude platforms against an expansion of a terrestrial radio accessnetwork; evaluation of whether the set of high altitude platforms willbe able to provide continuous service in the one or more geographicregions; evaluation of a staging criteria associated with the set ofhigh altitude platforms; or estimation of a navigation efficiency foreach platform of the set of high altitude platforms.
 17. The system ofclaim 16, wherein calculation of the cost of service for each subregionincludes performing an analysis of a total number of high altitudeplatforms required to serve potential customers of the uncoveredpopulation density over a selected period of time.
 18. The system ofclaim 16, wherein evaluation of whether the set of high altitudeplatforms will be able to provide continuous service includesdetermining an overprovisioning strategy for the set of high altitudeplatforms.
 19. The system of claim 15, wherein determination of themobile network expansion potential scores for each of the one or moregeographic regions further includes evaluating access networkinformation.
 20. A non-transitory computer-readable medium havinginstructions stored thereon, the instruction, when executed by one ormore processors, cause the one or more processors to implement a methodfor determining mobile network expansion using a high altitudeplatform-based communication system, the method comprising: identifyingan uncovered population density in one or more geographic regions;evaluating one or more atmospheric models for operation of a set of highaltitude platforms in the stratosphere; determining, based on theidentified uncovered population density in the one or more geographicregions and the evaluated one or more atmospheric models for the set ofhigh altitude platforms, mobile network expansion potential scores foreach of the one or more geographic regions; generating, forimplementation of a mobile network expansion, information representingthe determined mobile network expansion potential scores for each of theone or more geographic regions; and outputting the generated informationrepresenting the determined mobile network expansion potential scoresfor presentation to one or more users.